I. Field of the Invention
The present invention relates generally to computer modeling and management of systems and, more particularly, to computer simulation techniques with real-time system monitoring of electrical system health and performance.
II. Background of the Invention
Computer models of complex systems enable improved system design, development, and implementation through techniques for off-line simulation of system operation. That is, system models can be created on computers and then “operated” in a virtual environment to assist in the determination of system design parameters. All manner of systems can be modeled, designed, and operated in this way, including machinery, factories, electrical power and distribution systems, processing plants, devices, chemical processes, biological systems, and the like. Such simulation techniques have resulted in reduced development costs and superior operation.
Design and production processes have benefited greatly from such computer simulation techniques, and such techniques are relatively well developed, but they have not been applied in real-time, e.g., for real-time operational monitoring and management. In addition, predictive failure analysis techniques do not generally use real-time data that reflect actual system operation. Greater efforts at real-time operational monitoring and management would provide more accurate and timely suggestions for operational decisions, and such techniques applied to failure analysis would provide improved predictions of system problems before they occur.
That is, an electrical network model that can age and synchronize itself in real-time with the actual facility's operating conditions is critical to obtaining predictions that are reflective of the system's reliability, availability, health and performance in relation to the life cycle of the system. Static systems simply cannot adjust to the many daily changes to the electrical system that occur at a facility (e.g., motors and pumps switching on or off, changes to on-site generation status, changes to utility electrical feed . . . etc.) nor can they age with the facility to accurately predict the required indices. Without a synchronization or aging ability, reliability indices and predictions are of little value as they are not reflective of the actual operational status of the facility and may lead to false conclusions. With such improved techniques, operational costs and risks can be greatly reduced.
For example, mission critical electrical systems, e.g., for data centers or nuclear power facilities, must be designed to ensure that power is always available. Thus, the systems must be as failure proof as possible, and many layers of redundancy must be designed in to ensure that there is always a backup in case of a failure. It will be understood that such systems are highly complex, a complexity made even greater as a result of the required redundancy. Computer design and modeling programs allow for the design of such systems by allowing a designer to model the system and simulate its operation. Thus, the designer can ensure that the system will operate as intended before the facility is constructed.
As with all analytical tools, predictive or otherwise, the manner in which data and results are communicated to the user is often as important as the choice of analytical tool itself. Ideally, the data and results are communicated in a fashion that is simple to understand while also painting a comprehensive and accurate picture for the user. For example, current technology often overburdens users with thousands of pieces of information per second from sensory data points that are distributed throughout the monitored electrical power system facility. Therefore, it is nearly impossible for facility operators, managers and technicians to digest and understand all the sensory data to formulate an accurate understanding of their relevance to the overall status and health of their mission critical power system operations.
Currently, no solution exists for intelligent filtering of real-time power system sensory data into an easy to comprehend visual presentation to help facility operators, managers and technicians quickly understand the current health of their power systems.